I was visualizing my vorticity vector field and notice that I am not able to see the pattern without zooming in as there are too many glyphs and are too packed.
Currently, I am using a calculator to combine X,Y,Z vorticity field into a single vector field using the calculator. Take a slice of it and do a glyph filter visualizing all points on the plane.
I notice that one possible way is to visualize a curved glyphs and scale up a little bit to make it more noticeable, but not sure how to do that. Does anyone know whats the steps to do that? Or any other suggestions?
TIA
Have you tried reducing the Maximum Number of Sample Points property in the Properties Panel when the Glyph filter is selected in the Pipeline Browser? You may also want to change the Scale Factor property to change the length of the glyphs.
Related
In Marks, click Size and there pops a slider where I can adjust the size of a shape. But how to accurately control the size, is there some property with numbers to accurately control it? I have two sheets to show something similar and I want to display exactly the same sized shapes.
If you want to ensure 'sizes' are the same across two worksheets, I'd suggest snapping the 'size' setting to the center on both, as this is the easiest option to select. You can then use a measure to set the size, if this is desirable, and then the difference in size will be relative on both worksheets.
There isn't a numerical value override for the size slider.
Ben is correct, there isn't yet a numerical value override for the slider. You can use parameters with Min/Max/Sum etc. and a variable to somewhat change the sizes but they have to have multiple entries per line. It is unfortunate that Tableau still doesn't get that people want both a 'relative' sizing system that uses numbers from the dataset and a 'static' sizing system that allows for shapes to be set to '11px' or something along those lines. Yes, you can control that kind of in the dashboard with a vertical and fill entire box etc; but that doesn't address the very real scenario where you want a user to be able to re-size on the fly. Just my two cents.
I ran into this today. Very annoying. Need to keep shapes the same size across all worksheets and therefore same on dashboard.
I'm not familiar with Local Binary Pattern (LBP), could anyone help me to know how to extract LBP features from facial images (I need a simple code example)?
While searching, I found this code, but I didn't understand it.
So first of all you need to split the face into a certain amount of
sections.
For each of these sections you then have to loop through the all of
the pixels contained within that section and get their value (grey scale or colour values).
For each pixel check the value of the pixels which border it in (diagonals and up down left and right) and save them
for each of the directions check if the colour value of. if the colour is greater than the original pixels value you can assign that value a 1 and if it is less you can assign it as a 0.
you should get a list of 1's and 0's from the previous steps. put these numbers together and it will be a large binary number, you should be able to convert this to decimal and you will have a number assigned for that pixel. save this number per pixel.
after you have got a decimal number for each pixel within a section you can average all of the values to get an average number for this section.
This may not be the best description of how this works so here is a useful picture which might help you.
There is an extractLBPFeatures function in the R2015b release of the Computer Vision System Toolbox for MATLAB.
I want to display overlapping boxplots using Sigmaplot 12. When I choose the scale for the x-axis as linear then the boxes do indeed overlap but are much too thin. See figure below. Of course they should be much wider.
When I choose the scale of the x-axis to be "category", then the boxes have the right width, but are arranged along each single x-value.
I want the position as in figure 1 and the width as in figure 2. I tried to resize the box in figure 1 but when I choose 100% in "bar width" than it still looks like Figure 1.
many thanks!
okay, I found the answer myself. In Sigmaplot, there is often the need to prepare "style"-columns, for example if you want to color your barcharts, you need a column that holds the specific color names.
For my boxplot example I needed a column that has the values for "width". These had to be quite large (2000) in order to have an effect. Why ? I have no idea. First I thought it would be because of the latitude values and that the program interprets the point as "1.000"s, but when I changed to values without decimals, it didn´t get better.
Well, here is the result in color.
Have fun !
I saw many Q&A here about squeezing space out of Matlab figures. However I want to squeeze space resulted from a possibly fixed aspect, i.e. to choose proper paper size for figure printing when aspect is fixed.
Quite often I work with DEM/map/image thus I use axis image. Now if I want to produce a high resolution image I do something like
set(gcf,'PaperUnits','inches','PaperPosition',[0 0 4 3])
print('-dpng','-r300','somefile.png')
as described in Matlab KB.
The problem here is to determine a proper aspect such that I can specify proper paper size that would leave no white/background stripes on either sides.
Apparently if I have a map (let's say 1000x2000 cells) with aspect ratio of 0.5, and I'm printing it on 4"x3" paper, I'll get background stripes on the sides. This is quite annoying as I'd prefer 1.5"x3" paper + axes & labels or so. Right now I have to manually adjust paper size.
This is inconvenient as I'd like a universal solution. For instance I may print a plot into file that I expect to occupy 4"x3" as well that has no fixed aspect. Or I may want to print a 3D figure. I'm aware of daspect and pbaspect, but how can I know how it is currently drawn?
Perhaps I can derive current 2D aspect from get(gca,'Position') and then scale it to my maximum allowed desired size (e.g., 4"x3") while respecting whether DataAspectRatioMode (?) property is set to manual. Is it the way to proceed or is there a better way?
I am not exactly sure if I understand your problem exactly, but I have used the following commands to create pdf images that are sized exactly to the size of the figure. I have used this for both 2D and 3D figures. The "handle" variable is simply your figure handle.
set(handle,'Units','inches');
set(handle,'PaperUnits','Inches','PaperPositionMode','auto');
P = get(handle,'Position');
set(handle,'PaperSize', [P(3),P(4)]);
I would like to check whether an image has a lot of homogeneous areas. Therefore I would like to get some kind of value of an image that declares a ratio for images depending on the amount/size of homogeneous areas (e.g. that value could have a range from 0 to 5).
Instead of a value there could be some kind of classification as well.
[many homogeneous areas -> value/class 5 ; few homogeneous areas -> value/class 0]
I would like to do that in perl. Is there a package/function or something like that?
What you want seems to be an area of image processing research which I am not familiar with. However, GraphicsMagick's mogrify utility has a -segment option:
Use -segment to segment an image by analyzing the histograms of the color components and identifying units that are homogeneous with the fuzzy c-means technique. The scale-space filter analyzes the histograms of the three color components of the image and identifies a set of classes. The extents of each class is used to coarsely segment the image with thresholding. The color associated with each class is determined by the mean color of all pixels within the extents of a particular class. Finally, any unclassified pixels are assigned to the closest class with the fuzzy c-means technique.
I don't know if this is any use to you. You might have to hit the library on this one, and read some research. You do have access to this through PerlMagick as well. However, it does not look like it gives access to the internals, but just produces an image based on parameters.
In my tests (without really understanding what the parameters do), photos turned entirely black, whereas PNG images with large areas of similar colors were reduced to a sort of an average color. Whether you can use that fact to develop a measure is an open question I am not going to investigate ;-)